Abstract
We present a growing-window variable-regularization recursive least squares (GW-VR-RLS) algorithm. Standard recursive least squares (RLS) uses a time-invariant regularization. More specifically, the inverse of the initial covariance matrix in classical RLS can be viewed as a regularization term, which weights the difference between the next state estimate and the initial state estimate. The present paper allows for time-varying in the weighting as well as what is being weighted. This extension can be used to modulate the speed of convergence of the estimates versus the magnitude of transient estimation errors. Furthermore, the regularization term can weight the difference between the next state estimate and a time-varying vector of parameters rather than the initial state estimate as is required in standard RLS.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
